Application of Machine Learning-Based Electrochemical Deposition Models to CMP Modeling

Ghulghazaryan, Ruben; Piliposyan, Davit; Shoyan, Misak T.; Nersisyan, H

doi:10.51408/1963-0051

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…FCVD.-Developed by Applied Materials, 8 the FCVD process deposits a high-quality liquid-like dielectric film on the wafer surface, allowing the film to easily flow into gaps, filling them without voids or seams. Compared to plasma CVD, FCVD demonstrates a better deposition profile in bottom-up gap-filling capability.…”

Section: Hdp-cvdmentioning

confidence: 99%

“…This allows us to apply NNs to the modeling of the pre-CMP surface profile, using as input the geometric characteristics of the underlying pattern. 8,9 In this paper, we apply an NN-based full-chip deposition model for predicting post-deposition profile heights and geometry characteristics for use in CMP modeling. We determined that, given the geometric characteristics of pre-deposition profiles, NN configurations with 2 hidden layers and 6-10 neurons per hidden layer are sufficient to accurately predict the surface characteristics of a profile's erosion, dishing, and width data.…”

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confidence: 99%

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Application of Neural Network-Based Oxide Deposition Models to CMP Modeling

Ghulghazaryan¹,

Piliposyan²,

Wilson

2019

ECS J. Solid State Sci. Technol.

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Numerous deposition processes are used in modern semiconductor manufacturing, including high-density plasma chemical vapor deposition (HDP-CVD), spin-on dielectric (SOD), flowable CVD (FCVD), and enhanced high aspect ratio processes (eHARP). Generation of high quality post-deposition surface profiles is crucial for chemical-mechanical planarization (CMP) model building, due to the complex nature of the CMP process and long-range effects in CMP. Measurements show complicated post-deposition surface profile height dependence on the underlying pattern for these deposition processes. While high-quality compact models exist for HDP-CVD and SOD processes, building compact models for FCVD and eHARP is a challenge, since they include several deposition and annealing steps, and show complicated behavior width respect to the underlying pattern. In this paper, we present the application of neural networks (NNs) to post-deposition surface profiles modeling for the above-mentioned deposition processes. Experiments showed that NNs should have at least two hidden layers and 6–10 neurons per hidden layer to capture the complexity of deposited surface profiles. The application of NNs to modeling surface profiles of deposition processes has shown that NNs provide a general approach for modeling surface profiles of deposition processes without long-range effects for CMP modeling, irrespective of the complexity of the deposition process.

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Section: Hdp-cvdmentioning

confidence: 99%

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confidence: 99%

Application of Neural Network-Based Oxide Deposition Models to CMP Modeling

Ghulghazaryan¹,

Piliposyan²,

Wilson

2019

ECS J. Solid State Sci. Technol.

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Super fine cerium hydroxide abrasives for SiO2 film chemical mechanical planarization performing scratch free

Son

Jeong

Kim

et al. 2021

Sci Rep

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Face-centered-cubic crystallized super-fine (~ 2 nm in size) wet-ceria-abrasives are synthesized using a novel wet precipitation process that comprises a Ce4+ precursor, C3H4N2 catalyst, and NaOH titrant for a synthesized termination process at temperature of at temperature of 25 °C. This process overcomes the limitations of chemical–mechanical-planarization (CMP)-induced scratches from conventional dry ceria abrasives with irregular surfaces or wet ceria abrasives with crystalline facets in nanoscale semiconductor devices. The chemical composition of super-fine wet ceria abrasives depends on the synthesis termination pH, that is, Ce(OH)4 abrasives at a pH of 4.0–5.0 and a mixture of CeO2 and Ce(OH)4 abrasives at a pH of 5.5–6.5. The Ce(OH)4 abrasives demonstrate better abrasive stability in the SiO2-film CMP slurry than the CeO2 abrasives and produce a minimum abrasive zeta potential (~ 12 mV) and a minimum secondary abrasive size (~ 130 nm) at the synthesis termination pH of 5.0. Additionally, the abrasive stability of the SiO2-film CMP slurry that includes super-fine wet ceria abrasives is notably sensitive to the CMP slurry pH; the best abrasive stability (i.e., a minimum secondary abrasive size of ~ 130 nm) is observed at a specific pH (6.0). As a result, a maximum SiO2-film polishing rate (~ 524 nm/min) is achieved at pH 6.0, and the surface is free of stick-and-slip type scratches.

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Application of Machine Learning-Based Electrochemical Deposition Models to CMP Modeling

Cited by 2 publications

References 6 publications

Application of Neural Network-Based Oxide Deposition Models to CMP Modeling

Application of Neural Network-Based Oxide Deposition Models to CMP Modeling

Super fine cerium hydroxide abrasives for SiO2 film chemical mechanical planarization performing scratch free

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